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Locating Common Styles Based Totally On Quantitative Binary Attributes Using FP-Growth Algorithm

Assistant professor, Rise Krishna Sai Gandhi Group of Institutions: Ongole,
:under process

Discovery of frequent patterns from outsized information is taken into account as a crucial facet of data mining. There is always associate degree ever increasing demand to search out the frequent patterns. This paper introduces a technique to handle the categorical attributes associate degree numerical attributes in an economical means. Within the planned methodology, the ordinary database is reborn into quantitative information and thus it's reborn into binary values reckoning on the condition of the coed information. From the binary patterns of all attributes bestowed within the student information, the frequent patterns are known exploitation FP-growth; the conversion reveals all the frequent patterns within the student database.

RAVULA KARTHEEK et al. ," Locating Common Styles Based Totally On Quantitative Binary Attributes Using FP-Growth Algorithm”, International Journal of Computer Engineering In Research Trends, 3(10):561-567,October-2016.

Keywords : Quantitative attributes, Data mining, FP-growth algorithm, frequent patterns.

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[7] J. Han, J. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation(PDF), (Slides), Proc. 2000 ACM-SIGMOD Int. May 2000.
[8] A.B.M.Rezbaul Islam, Tae-Sun Chung An Improved Frequent Pattern Tree Based Association Rule Mining Techniques Department of Computer Engineering Ajou University Suwon, Republic of Korea
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[10] E. Ramaraj and N. Venkatesan, ― Bit Stream Mask Search Algorithm in Frequent Itemset Mining,‖ European Journal of Scientific Research,‖ Vol. 27 No.2 (2009).
[11] Qihua Lan, Defu Zhang, Bo Wu ,A New Algorithm For Frequent Itemsets Mining Based On Apriori And FP-Tree,Department of Computer Science, Xiamen University, Xiamen China 2009 IEEE

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